feat: update Sleep Health Notebook

inference/app.py

@@ -80,7 +80,8 @@ async def startup():
             
             # Better background data prep for SHAP
             bg_df = df.head(50).copy()
-            if tg in bg_df.columns: bg_df = bg_df.drop(columns=[tg])
+            ignore = [tg.lower(), "person_id", "id", "customer_id", "driver_id"]
+            bg_df = bg_df.drop(columns=[c for c in bg_df.columns if c.lower() in ignore], errors="ignore")
             
             # Ensure numeric columns are numeric and others are dummy encoded
             for c in bg_df.columns:
@@ -149,7 +150,7 @@ async def fields(id: str):
     tg = art["target"].get("target_col")
     out = []
     for c in df.columns:
-        if c == tg: continue
+        if c == tg or c.lower() in ["person_id", "id", "customer_id", "driver_id"]: continue
         t = "number" if pd.api.types.is_numeric_dtype(df[c].dtype) else "select"
         opts = [x for x in df[c].unique().tolist() if pd.notna(x)] if t == "select" else []
         out.append({"name": c, "type": t, "options": clean(opts), "default": ""})
@@ -171,7 +172,8 @@ async def run(id: str, req: PredictRequest):
         raw = req.features
         cols = art["df"].columns
         tg = art["target"].get("target_col")
-        row = {c: raw.get(c, art["df"][c].iloc[0]) for c in cols if c != tg}
+        ignore = [tg.lower(), "person_id", "id", "customer_id", "driver_id"]
+        row = {c: raw.get(c, art["df"][c].iloc[0]) for c in cols if c.lower() not in ignore}
         df = pd.DataFrame([row])
         
         for c in df.columns:

notebooks/Sleep_Health_And_Daily_Performance.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "id": "d8d06600",
    "metadata": {},
    "outputs": [],
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "id": "5a9b209d",
    "metadata": {},
    "outputs": [
@@ -80,18 +80,7 @@
        "      <th>sleep_quality_score</th>\n",
        "      <th>rem_percentage</th>\n",
        "      <th>deep_sleep_percentage</th>\n",
-       "      <th>sleep_latency_mins</th>\n",
-       "      <th>wake_episodes_per_night</th>\n",
-       "      <th>caffeine_mg_before_bed</th>\n",
-       "      <th>alcohol_units_before_bed</th>\n",
-       "      <th>screen_time_before_bed_mins</th>\n",
-       "      <th>exercise_day</th>\n",
-       "      <th>steps_that_day</th>\n",
-       "      <th>nap_duration_mins</th>\n",
-       "      <th>stress_score</th>\n",
-       "      <th>work_hours_that_day</th>\n",
-       "      <th>chronotype</th>\n",
-       "      <th>mental_health_condition</th>\n",
+       "      <th>...</th>\n",
        "      <th>heart_rate_resting_bpm</th>\n",
        "      <th>sleep_aid_used</th>\n",
        "      <th>shift_work</th>\n",
@@ -117,18 +106,7 @@
        "      <td>6.6</td>\n",
        "      <td>22.5</td>\n",
        "      <td>19.3</td>\n",
-       "      <td>16</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>32</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6592</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.4</td>\n",
-       "      <td>10.7</td>\n",
-       "      <td>Morning</td>\n",
-       "      <td>Healthy</td>\n",
+       "      <td>...</td>\n",
        "      <td>63</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -152,18 +130,7 @@
        "      <td>6.9</td>\n",
        "      <td>26.9</td>\n",
        "      <td>14.9</td>\n",
-       "      <td>17</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>33</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10111</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>Neutral</td>\n",
-       "      <td>Healthy</td>\n",
+       "      <td>...</td>\n",
        "      <td>52</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
@@ -187,18 +154,7 @@
        "      <td>1.0</td>\n",
        "      <td>20.2</td>\n",
        "      <td>16.2</td>\n",
-       "      <td>26</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>89</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9222</td>\n",
-       "      <td>28</td>\n",
-       "      <td>7.8</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>Neutral</td>\n",
-       "      <td>Both</td>\n",
+       "      <td>...</td>\n",
        "      <td>72</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
@@ -222,18 +178,7 @@
        "      <td>6.4</td>\n",
        "      <td>17.7</td>\n",
        "      <td>17.7</td>\n",
-       "      <td>13</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>52</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9190</td>\n",
-       "      <td>40</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>Morning</td>\n",
-       "      <td>Healthy</td>\n",
+       "      <td>...</td>\n",
        "      <td>71</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -257,18 +202,7 @@
        "      <td>3.2</td>\n",
        "      <td>23.3</td>\n",
        "      <td>18.3</td>\n",
-       "      <td>30</td>\n",
-       "      <td>5</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>72</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4273</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.4</td>\n",
-       "      <td>10.4</td>\n",
-       "      <td>Neutral</td>\n",
-       "      <td>Healthy</td>\n",
+       "      <td>...</td>\n",
        "      <td>71</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -282,6 +216,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 32 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -299,54 +234,28 @@
        "3                6.79                  6.4            17.7   \n",
        "4                5.02                  3.2            23.3   \n",
        "\n",
-       "   deep_sleep_percentage  sleep_latency_mins  wake_episodes_per_night  \\\n",
-       "0                   19.3                  16                        3   \n",
-       "1                   14.9                  17                        4   \n",
-       "2                   16.2                  26                        4   \n",
-       "3                   17.7                  13                        4   \n",
-       "4                   18.3                  30                        5   \n",
-       "\n",
-       "   caffeine_mg_before_bed  alcohol_units_before_bed  \\\n",
-       "0                       0                       0.0   \n",
-       "1                       0                       0.0   \n",
-       "2                       0                       2.0   \n",
-       "3                       0                       1.0   \n",
-       "4                      40                       0.0   \n",
-       "\n",
-       "   screen_time_before_bed_mins  exercise_day  steps_that_day  \\\n",
-       "0                           32             0            6592   \n",
-       "1                           33             1           10111   \n",
-       "2                           89             1            9222   \n",
-       "3                           52             1            9190   \n",
-       "4                           72             0            4273   \n",
-       "\n",
-       "   nap_duration_mins  stress_score  work_hours_that_day chronotype  \\\n",
-       "0                  0           4.4                 10.7    Morning   \n",
-       "1                  8           4.0                  3.0    Neutral   \n",
-       "2                 28           7.8                  3.6    Neutral   \n",
-       "3                 40           4.9                  6.7    Morning   \n",
-       "4                  0           7.4                 10.4    Neutral   \n",
+       "   deep_sleep_percentage  ...  heart_rate_resting_bpm  sleep_aid_used  \\\n",
+       "0                   19.3  ...                      63               0   \n",
+       "1                   14.9  ...                      52               1   \n",
+       "2                   16.2  ...                      72               0   \n",
+       "3                   17.7  ...                      71               0   \n",
+       "4                   18.3  ...                      71               0   \n",
        "\n",
-       "  mental_health_condition  heart_rate_resting_bpm  sleep_aid_used  shift_work  \\\n",
-       "0                 Healthy                      63               0           0   \n",
-       "1                 Healthy                      52               1           0   \n",
-       "2                    Both                      72               0           1   \n",
-       "3                 Healthy                      71               0           0   \n",
-       "4                 Healthy                      71               0           0   \n",
+       "   shift_work  room_temperature_celsius  weekend_sleep_diff_hrs  season  \\\n",
+       "0           0                      20.1                    1.84  Autumn   \n",
+       "1           0                      18.0                    0.13  Winter   \n",
+       "2           1                      17.9                    1.67  Spring   \n",
+       "3           0                      19.1                    2.37  Summer   \n",
+       "4           0                      19.7                    1.26  Summer   \n",
        "\n",
-       "   room_temperature_celsius  weekend_sleep_diff_hrs  season day_type  \\\n",
-       "0                      20.1                    1.84  Autumn  Weekday   \n",
-       "1                      18.0                    0.13  Winter  Weekend   \n",
-       "2                      17.9                    1.67  Spring  Weekend   \n",
-       "3                      19.1                    2.37  Summer  Weekend   \n",
-       "4                      19.7                    1.26  Summer  Weekday   \n",
+       "   day_type  cognitive_performance_score  sleep_disorder_risk  felt_rested  \n",
+       "0   Weekday                         73.4              Healthy            0  \n",
+       "1   Weekend                         99.4              Healthy            1  \n",
+       "2   Weekend                          2.5               Severe            0  \n",
+       "3   Weekend                         67.8              Healthy            0  \n",
+       "4   Weekday                         38.1                 Mild            0  \n",
        "\n",
-       "   cognitive_performance_score sleep_disorder_risk  felt_rested  \n",
-       "0                         73.4             Healthy            0  \n",
-       "1                         99.4             Healthy            1  \n",
-       "2                          2.5              Severe            0  \n",
-       "3                         67.8             Healthy            0  \n",
-       "4                         38.1                Mild            0  "
+       "[5 rows x 32 columns]"
       ]
      },
      "metadata": {},
@@ -386,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "id": "e716742b",
    "metadata": {},
    "outputs": [
@@ -394,14 +303,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X_train shape: (80000, 67)\n",
-      "X_test shape: (20000, 67)\n",
+      "X_train shape: (80000, 66)\n",
+      "X_test shape: (20000, 66)\n",
       "Classes: ['Healthy', 'Mild', 'Moderate', 'Severe']\n"
      ]
     }
    ],
    "source": [
-    "X = df.drop(columns=[target_column])\n",
+    "X = df.drop(columns=[target_column, 'person_id'])\n",
     "y_text = df[target_column].astype(str)\n",
     "\n",
     "label_encoder = LabelEncoder()\n",
@@ -433,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "id": "4bab4630",
    "metadata": {},
    "outputs": [
@@ -477,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "id": "edfa16c9",
    "metadata": {},
    "outputs": [
@@ -485,25 +394,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 0.953\n",
-      "Weighted Precision: 0.9517\n",
-      "Weighted Recall: 0.953\n",
-      "Weighted F1 Score: 0.9521\n",
-      "Weighted ROC AUC (OvR): 0.997\n",
-      "Log Loss: 0.1405\n",
+      "Accuracy: 0.9525\n",
+      "Weighted Precision: 0.9511\n",
+      "Weighted Recall: 0.9525\n",
+      "Weighted F1 Score: 0.9514\n",
+      "Weighted ROC AUC (OvR): 0.9969\n",
+      "Log Loss: 0.1408\n",
       "\n",
       "Classification Report:\n",
       "\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "     Healthy     0.9980    0.9983    0.9982     10831\n",
-      "        Mild     0.9373    0.9661    0.9515      6696\n",
-      "    Moderate     0.7452    0.6976    0.7206      1660\n",
-      "      Severe     0.8759    0.7638    0.8160       813\n",
+      "     Healthy     0.9979    0.9982    0.9980     10831\n",
+      "        Mild     0.9357    0.9668    0.9510      6696\n",
+      "    Moderate     0.7439    0.6928    0.7174      1660\n",
+      "      Severe     0.8773    0.7565    0.8124       813\n",
       "\n",
-      "    accuracy                         0.9530     20000\n",
-      "   macro avg     0.8891    0.8565    0.8716     20000\n",
-      "weighted avg     0.9517    0.9530    0.9521     20000\n",
+      "    accuracy                         0.9525     20000\n",
+      "   macro avg     0.8887    0.8536    0.8697     20000\n",
+      "weighted avg     0.9511    0.9525    0.9514     20000\n",
       "\n"
      ]
     }
@@ -537,13 +446,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "id": "b22a6a06",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x600 with 2 Axes>"
       ]
@@ -574,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "id": "38fd3179",
    "metadata": {},
    "outputs": [
@@ -647,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "id": "fb9802ef",
    "metadata": {},
    "outputs": [
@@ -680,32 +589,32 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Accuracy</td>\n",
-       "      <td>0.953050</td>\n",
+       "      <td>0.952500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Weighted Precision</td>\n",
-       "      <td>0.951701</td>\n",
+       "      <td>0.951070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Weighted Recall</td>\n",
-       "      <td>0.953050</td>\n",
+       "      <td>0.952500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Weighted F1 Score</td>\n",
-       "      <td>0.952081</td>\n",
+       "      <td>0.951443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Weighted ROC AUC (OvR)</td>\n",
-       "      <td>0.996961</td>\n",
+       "      <td>0.996892</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>Log Loss</td>\n",
-       "      <td>0.140510</td>\n",
+       "      <td>0.140831</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -713,12 +622,12 @@
       ],
       "text/plain": [
        "                   Metric     Value\n",
-       "0                Accuracy  0.953050\n",
-       "1      Weighted Precision  0.951701\n",
-       "2         Weighted Recall  0.953050\n",
-       "3       Weighted F1 Score  0.952081\n",
-       "4  Weighted ROC AUC (OvR)  0.996961\n",
-       "5                Log Loss  0.140510"
+       "0                Accuracy  0.952500\n",
+       "1      Weighted Precision  0.951070\n",
+       "2         Weighted Recall  0.952500\n",
+       "3       Weighted F1 Score  0.951443\n",
+       "4  Weighted ROC AUC (OvR)  0.996892\n",
+       "5                Log Loss  0.140831"
       ]
      },
      "metadata": {},

outputs/Sleep_Health_And_Daily_Performance/metrics/final_metircs.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1ed4f6ea761170cc001686ec6016e741bcf8dce4da7fb8360a83647a3f771f47
-size 198
+oid sha256:c553da4b4a278f4054919f87c6e4b99a4b867d5fc977f134274a1b7687337faf
+size 193

outputs/Sleep_Health_And_Daily_Performance/metrics/prediction.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b10d56959f144df9f33a0c65dfab6c1a1aac9109369ad5429df10d4c3466dd7b
-size 300916
+oid sha256:f01ccd727b7fe2b919513147272d1430c7e07bd8ba7e6317dd20052a9c361860
+size 300865

outputs/Sleep_Health_And_Daily_Performance/model/feature_columns.json

@@ -1,5 +1,4 @@
 [
-  "person_id",
   "age",
   "bmi",
   "sleep_duration_hrs",

outputs/Sleep_Health_And_Daily_Performance/model/model.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2916ff9b330c54e63af09d56ffdb0a7c1856594a7ce7fb107f8c083a951b8a4a
-size 5449716
+oid sha256:b0c2358f96b7f3a32887567b5ef82347634e4b15284102ab2c72dd7b77fbcea6
+size 5449616